Presentation
NeuVAS: Neural Implicit Surfaces for Variational Shape Modeling
DescriptionNeural implicit shape representation has drawn significant attention in recent years due to its continuity, differentiability, and topological flexibility. However, directly modeling the shape of neural implicit fields, especially the neural signed distance function (SDF), with sparse geometric control is still a challenging task. While 3D curve networks can provide intuitive control over explicit surfaces, the sparsity and varied topology of these networks introduce ambiguity in surface shape interpolation and present challenges in mesh layout design under curve constraints. Consequently, achieving reasonable surfacing from curve networks has long been a challenge in mesh modeling. In this paper, we propose NeuVAS, a curvature-based approach
to solve the neural SDF under curve network constraints. Leveraging the differentiability of neural shape representations, we introduce a smooth term to regularize the zero-level surface of the SDF, providing dense control over shape interpolation. Typically, a reasonable surface interpolated from a curve network consists of piecewise smooth surface patches that are 𝐶0 continuous at curve constraints. However, encoding such a shape using a neural SDF poses significant challenges. To construct piecewise smoothness on neural SDFs, we minimize an optional smooth term based on curvature in the space between the curves while relaxing this constraint near feature curves. Moreover, our method can accommodate either structured curve
networks or oriented point clouds as input constraints, making it applicable to a broad range of scenarios. A comprehensive comparison with existing state-of-the-art methods demonstrates the significant advantages of our approach in surfacing curve networks.
to solve the neural SDF under curve network constraints. Leveraging the differentiability of neural shape representations, we introduce a smooth term to regularize the zero-level surface of the SDF, providing dense control over shape interpolation. Typically, a reasonable surface interpolated from a curve network consists of piecewise smooth surface patches that are 𝐶0 continuous at curve constraints. However, encoding such a shape using a neural SDF poses significant challenges. To construct piecewise smoothness on neural SDFs, we minimize an optional smooth term based on curvature in the space between the curves while relaxing this constraint near feature curves. Moreover, our method can accommodate either structured curve
networks or oriented point clouds as input constraints, making it applicable to a broad range of scenarios. A comprehensive comparison with existing state-of-the-art methods demonstrates the significant advantages of our approach in surfacing curve networks.
Authors
Event Type
Technical Papers
TimeMonday, 15 December 20253:11pm - 3:22pm HKT
LocationMeeting Room S221, Level 2